Disparities in multiple myeloma (MM) prognosis based on sociodemographic factors may exist. We investigated whether education level at diagnosis influenced Chinese MM patient outcomes.
Xu et al BMC Cancer (2020) 20:737 https://doi.org/10.1186/s12885-020-07178-5 RESEARCH ARTICLE Open Access Education level as a predictor of survival in patients with multiple myeloma Limei Xu1, Xiuju Wang2, Xueyi Pan3, Xiaotao Wang4, Qing Wang5, Bingyi Wu6, Jiahui Cai6, Ying Zhao7, Lijuan Chen8, Wuping Li9 and Juan Li1* Abstract Background: Disparities in multiple myeloma (MM) prognosis based on sociodemographic factors may exist We investigated whether education level at diagnosis influenced Chinese MM patient outcomes Methods: We performed a multicenter retrospective analysis of data from 773 MM patients across centers in China from 2006 to 2019 Sociodemographic and clinical factors at diagnosis and treatment regimens were recorded, and univariate and multivariate analyses were performed Results: Overall, 69.2% of patients had low education levels Patients with low education levels differed from those with high education levels in that they were more likely to be older, and a higher proportion lived in rural areas, were unemployed, had lower annual incomes and lacked insurance Additionally, compared to patients with high education levels, patients with low education levels had a higher proportion of international staging system (ISS) stage III classification and elevated lactate dehydrogenase (LDH) levels and underwent transplantation less often Patients with high education levels had a median progression-free survival (PFS) of 67.50 (95% confidence interval (CI): 51.66–83.39) months, which was better than that of patients with low education levels (30.60 months, 95% CI: 27.38–33.82, p < 0.001) Similarly, patients with high education levels had a median overall survival (OS) of 122.27 (95% CI: 117.05–127.49) months, which was also better than that of patients with low education levels (58.83 months, 95% CI: 48.87–62.79, p < 0.001) In the multivariable analysis, patients with high education levels had lower relapse rates and higher survival rates than did those with low education level in terms of PFS and OS (hazard ratio (HR) = 0.50 [95% CI: 0.34–0.72], p < 0.001; HR = 0.32 [0.19–0.56], p < 0.001, respectively) Conclusions: Low education levels may independently predict poor survival in MM patients in China Keywords: Education level, Sociodemographic status, Multiple myeloma, Survival prognosis Background Multiple myeloma (MM) is characterized by the clonal proliferation of malignant plasma cells, causing lytic skeletal lesions, renal failure, hypercalcemia, and anemia, and patients typically present with monoclonal protein in the serum and/or urine [1, 2] Currently, MM is the second-most common malignancy of the blood in many * Correspondence: juanlihematology@163.com Department of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China Full list of author information is available at the end of the article countries and has been estimated to account for 1.82% of all malignancies and 18% of all hematological malignancies, according to data from the United States [3, 4] In recent years, with the continuous advent of new drugs and new treatments, the prognosis of patients with MM has been greatly improved However, not all MM patients benefit equally from these improvements [5] To explore the causes of this difference, a few studies from the Cancer Registry and the SEER database have shown the impact of racial and socioeconomic status (SES) disparities on the prognosis of patients with © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data Xu et al BMC Cancer (2020) 20:737 multiple myeloma [6–10] Some studies have reported a significant increase in the risk of MM in individuals with low SES [8–10] In addition, some studies reported differences in the clinical characteristics, incidence and survival prognosis among patients with MM across racial and ethnic groups [6], while some studies showed no consistent association between race/ethnicity or SES and survival outcomes after adjustment for confounders [7, 11, 12] Globally, compared with the United States and other high-income countries, low- or middle-income countries have slower regulatory approval of drugs, fewer types of drugs available, and higher drug prices when adjusted for gross domestic product per capita; thus, the chances of effective treatment for these MM patients are greatly reduced [13, 14] However, the mortality of MM in China, a country with a large population, has increased in recent years, especially in rural areas [15] The impact of demographic and socioeconomic factors on the prognosis and survival of patients with MM has not been reported in developing countries such as China Education level is an important factor in patients’ demography To understand the relationship between the education level and survival prognosis of Chinese MM patients, demographic factors (e.g., education level, occupational status, income, place of residence, marital status) and clinical characteristics (e.g., initial disease staging, lactate dehydrogenase (LDH) level, cytogenetics, comorbidities) at diagnosis and treatment regimens (e.g., underwent transplantation) were recorded and analyzed Methods Patients This retrospective, multicenter study was conducted in centers across several provinces in China A total of 773 newly diagnosed MM patients were enrolled in this study from January 2006 to July 2019 at each of the participating institutions In accordance with the diagnostic criteria for multiple myeloma and disease progression, eligible patients were defined according to standard International Myeloma Working Group criteria [16, 17] The treatment of patients was divided into transplantation and nontransplantation Progression-free survival (PFS) was calculated from the time of the initial diagnosis of MM to disease progression, death or the last follow-up, and overall survival (OS) was calculated from the time of the initial diagnosis of MM until death or the last follow-u Sociodemographic and clinical variables We analyzed the personal information and clinical information of each patient at the time of the first visit, including age, sex, smoking status (yes or no), marital status (married, single, divorced or widowed), place of residence (urban or rural), the distance between place of Page of 10 residence and the hospital (in the same or different provinces), insurance status (insured or uninsured), and annual household income ( month 592 (76.6) ISS stage Marital status Married 742 (96.0) I/II 531 (68.7) Unmarried (1.2) III 226 (29.2) Divorced 13 (1.7) Unknown 16 (2.1) Widowed (1.1) < 240 U/L 565 (73.1) 552 (71.4) ≥240 U/L 144 (18.6) 221 (28.6) Unknown 64 (8.3) Residential area Urban Rural LDH level Cytogenetic abnormality by FISH Distance to hospital In the same province 668 (86.4) High risk In a different province 105 (13.6) Standard risk 429 (55.5) Unknown 208 (26.9) Education level Low education level 535 (69.2) High education level 226 (29.2) Unknown 12 (1.6) 136 (17.6) Receipt of transplant Yes 283 (36.6) No 490 (63.4) Regular treatment Occupational status 144 (18.6) Yes 524 (67.8) Unemployed 605 (78.3) No 249 (32.2) Unknown 24 (3.1) Employed ISS international staging system, LDH lactate dehydrogenase, FISH fluorescence in situ hybridization Average annual income ≤ $42,500 USD 595 (77.0) > $42,500 USD 117 (15.1) Unknown 61 (7.9) Insurance status Any insurance 174 (22.5) No insurance 536 (69.3) Unknown 63 (8.2) Initial symptoms Bone pain 508 (65.7) Anemia 118 (15.3) Infection 34 (4.4) Anesthesia 11 (1.4) Renal insufficiency 47 (6.1) Others 55 (7.1) and renal function impairment Additionally, 38.5% of patients had cardiovascular disease and/or metabolic syndrome, and 4.7% had other tumors The time from onset to definite diagnosis varied with most of the patients receiving a definite diagnosis after more than month (76.6%), and 29.2% of the patients had ISS stage III disease at the time of onset A total of 18.6% of the patients had LDH levels greater than 240 U/L, and 17.6% of the patients had high-risk cytogenetics Moreover, 36.6% of patients underwent transplantation and 67.8% of the patients received regular treatment and underwent regular follow-up Comparison between MM patients with a high vs low education level Information on education level was available for 98.5% of the patients (761/773) Patients with low education Xu et al BMC Cancer (2020) 20:737 levels were more likely to be older (≥60 years, 51.2% vs 36.3%, p < 0.001), and a higher proportion were female (46.9% vs 35.0%, p = 0.002), lived in rural areas (39.1% vs 5.3%, p < 0.001), were unemployed (86.9% vs 66.2%, p < 0.001), had a lower income (94.5% vs 59.3%, p < 0.001), lacked insurance (82.2% vs 60.4%, p < 0.001) and had comorbidities (32.3% vs 43.8%, p = 0.003) Additionally, time to diagnosis > month was more frequent in patients with low education levels (81.3% vs 65.0%, p < 0.001), and they consistently had a higher ISS stage (III, 32.5% vs 23.7%, p = 0.014) and elevations in LDH levels (≥240 U/L, 23.1% vs 13.0%, p = 0.003) However, there was no difference in cytogenetics between the two groups In addition, patients with high education levels were more likely to be treated via transplantation (59.3% vs 27.9%, p < 0.001) and undergo regular treatment (87.6% vs 60.7%, p < 0.001) than patients with low education levels (Table 2) Univariate analyses for PFS and OS The median follow-up for the entire cohort was 29.6 months (range, 0.3 months to 162.8 months) from the start of diagnosis Kaplan-Meier analyses showed that the median PFS and OS for all patients were, respectively, 39.93(95% CI: 35.79–44.07) months and 79.63 (95% CI: 58.88–100.48) months (Fig 1a, b) Patients with high education levels had a median PFS of 67.50 (95% CI: 51.66–83.39) months, which was better than that of patients with low education levels (30.60 months, 95% CI: 27.38–33.82, p < 0.001, Fig 1c) Similarly, patients with high education levels had a median OS of 122.27 (95% CI: 117.05–127.49) months, which was also better than that of patients with low education levels (58.83 months, 95% CI: 48.87–62.79, p < 0.001, Fig 1d) In this study, univariate Cox regression analyses were performed to explore the association between the baseline factors of patients and PFS and OS The sociodemographic factors associated with worse PFS and OS in the univariate Cox regression model included age (HR = 1.04 [95% CI: 1.02–1.04]; HR = 1.03[95% CI: 1.02–1.05], respectively), residence in a rural setting (HR = 1.48[95% CI: 1.14–1.93]; HR = 1.47[95% CI: 1.06–2.05], respectively), living in a different province from the treating hospital (HR = 1.18[95% CI: 1.01–1.37]; HR = 1.15[95% CI: 0.94–1.41], respectively), being unemployed (HR = 1.67[1.22–2.30]; HR = 2.53[1.55–4.13], respectively), and a lack of insurance (HR = 1.54[95% CI: 1.15–2.06]; HR = 2.16[95% CI: 1.43–3.29], respectively) Additional clinical factors associated with worse PFS and OS included complications at diagnosis (HR = 1.72[95% CI: 1.35–2.18]; HR = 2.54[95% CI: 1.81–3.56], respectively), time to diagnosis > month (HR = 1.47[95% CI: 1.13–1.91]; HR = 1.96[95% CI: 1.37–2.81], respectively), ISS stage III disease (HR = 1.23[95% CI: 1.09–1.39]; HR = 1.38[95% CI: Page of 10 1.19–1.60], respectively), elevations in LDH levels (HR = 1.87[95% CI: 1.43–2.46]; HR = 1.85[95% CI: 1.32–2.60], respectively), high-risk cytogenetics (HR = 1.68[95% CI: 1.26–2.25]; HR = 1.98[95% CI: 1.38–2.82], respectively), no transplantation (HR = 2.98[95% CI: 2.34–3.80]; HR = 2.53[95% CI: 1.87–3.44], respectively), and irregular treatment (HR = 3.28[95% CI: 2.59–4.16]; HR = 3.51[95% CI: 2.61–4.71], respectively) In addition, sociodemographic factors associated with better PFS and OS in the univariate Cox regression model included a high education level (HR = 0.39[95% CI: 0.30–0.52]; HR = 0.25[95% CI: 0.17–0.38], respectively) and a high annual income (i.e., ≥ $42,500; HR = 0.51[95% CI: 0.37–0.70]; HR = 0.36[95% CI: 0.23–0.55], respectively) (Table 3) Multivariate analyses for PFS and OS To further analyze the influence of sociodemographic factors on patient survival, multivariate Cox regression analyses were conducted Since age is an important factor affecting survival and we found that education and age have interactive effects on survival, we analyzed the effects of demographic and clinical factors on PFS and OS in patients with MM by dividing them into groups of patients < 60 years old and ≥ 60 years old We found that in different age groups, education level, LDH levels, cytogenetics and receipt of transplant were independently associated with PFS, while in the age stratification analysis, regular treatment was an independent factor affecting the PFS of patients < 60 years old (Table 4) In addition, for all patients, the independent risk factors affecting OS included patients` age (per year of age), low education level, elevated LDH level, high-risk cytogenetics, complications at diagnosis and irregular treatment In the analysis of age stratification, for patients younger than 60 years old, education level, cytogenetics and regular treatment were independent prognostic factors for OS Additionally, for patients ≥60 years old, education level, LDH levels, cytogenetics and complications at diagnosis were independent prognostic factors for OS (Table 5) Discussion To the best of our knowledge, this study is the first to examine the relationship between sociodemographic factors and survival in patients with MM in China The prognostic factors of MM mainly include host factors, tumor characteristics and treatment methods [19] A single factor is often not enough to determine the prognosis Among the tumor factors, we usually evaluate the prognosis of patients by ISS stage, LDH level and cytogenetics Moreover, in terms of treatment, we also found that hematopoietic stem cell transplantation in patients with MM can significantly improve the survival prognosis [20] However, there is no consensus on the impact Xu et al BMC Cancer (2020) 20:737 Page of 10 Table Comparison of demographic and clinical characteristics between patients with high and low education levels Variables Low education level N = 535 High education level (%) N = 226 P (%) Age < 0.001 < 60 261 48.8 144 63.7 ≥ 60 274 51.2 82 36.3 Male 284 53.1 147 65.0 Female 251 46.9 79 35.0 Sex 0.002 Smoking 0.183 Yes 158 29.5 56 24.8 No 377 70.5 170 75.2 Married 515 96.3 216 95.6 Other 20 3.7 10 4.4 Marital status 0.657 Residential area < 0.001 Urban 326 60.9 214 94.7 Rural 209 39.1 12 5.3 Same province 458 85.6 200 88.5 Different province 77 14.4 26 11.5 Distance to hospital 0.287 Occupational status < 0.001 Employed 68 13.1 76 33.8 Unemployed 453 86.9 149 66.2 < $42,500 USD 464 94.5 131 59.3 ≥ $42,500 USD 27 5.5 90 40.7 Average annual income < 0.001 Insurance status < 0.001 Any insurance 88 17.8 86 39.6 No insurance 405 82.2 131 60.4 Yes 362 67.7 127 56.2 No 173 32.3 99 43.8 Comorbidity < 0.003 Time to diagnosis < 0.001 ≤ month 100 18.7 79 35.0 > months 435 81.3 147 65.0 I/II 351 67.5 172 76.4 III 169 32.5 53 23.6 ≥ 240 U/L 115 23.1 26 13.0 < 240 U/L 383 76.9 174 87.0 High risk 96 24.5 38 23.3 Standard risk 296 75.5 125 76.7 ISS stage 0.014 LDH Cytogenetics 0.003 0.768 Xu et al BMC Cancer (2020) 20:737 Page of 10 Table Comparison of demographic and clinical characteristics between patients with high and low education levels (Continued) Variables Low education level N = 535 High education level (%) N = 226 P (%) Receipt of transplant < 0.001 Yes 149 27.9 134 59.3 No 386 72.2 92 40.7 Yes 325 60.7 198 87.6 No 210 39.3 28 12.4 Regular treatment < 0.001 ISS international staging system, LDH lactate dehydrogenase of patient host factors on prognosis To date, the prognosis of patients has not been evaluated with these three factors at the same time Therefore, we included demographic factors (e.g., age, sex, education level, income, work, insurance), tumor characteristics (e.g., ISS stage, cytogenetics, LDH level) and treatment methods in the analysis SES is often measured by income, education or occupation, either as singular variables or in combination, which is a strong predictor for survival prognoses in MM as well as other diseases [6, 8, 21, 22] It can be assumed that the education level covaries with SES Cancer death rates vary considerably by level of education [23] Attalla, K et al found that penile cancer patients with low education levels were more likely to be diagnosed with a worse pathologic T stage [24] Hwang, K.T et al found that high education levels conferred a superior prognosis for breast cancer patients in the subgroup aged > 50 years; these patients had a lower mean age at the first diagnosis and more favorable biological features [25] In our study, we set income, education level and occupational status as independent factors As age and Fig Kaplan-Meier plots of PFS and OS for MM patients a The median PFS for 773 MM patients b The median OS for 773 MM patients c Kaplan-Meier plots of PFS were compared between MM patients with high and low education levels d Kaplan-Meier plots of OS were compared between MM patients with high and low education levels Xu et al BMC Cancer (2020) 20:737 Page of 10 Table Univariate analysis of the baseline parameters associated with PFS and OS Variable PFS OS HR (95% CI) P HR (95% CI) 1.04 (0.83–1.30) 0.74 0.97 (0.73–1.29) 0.823 1.03 (1.02–1.04) < 0.001 1.04 (1.02–1.05) < 0.001 0.95 (0.74–1.23) 0.71 0.93 (0.68–1.29) 0.671 0.78 (0.44–1.34) 0.41 0.85 (0.40–1.81) 0.672 1.48 (1.14–1.93) 0.003 1.47 (1.06–2.05) 0.023 1.18 (1.01–1.37) 0.039 1.15 (0.94–1.41) 0.175 0.39 (0.30–0.52) < 0.001 0.25 (0.17–0.38) < 0.001 1.67 (1.22–2.30) 0.002 2.53 (1.55–4.13) < 0.001 0.51 (0.37–0.70) < 0.001 0.36 (0.23–0.55) < 0.001 1.54 (1.15–2.06) 0.004 2.16 (1.43–3.29) < 0.001 1.72 (1.35–2.18) < 0.001 2.54 (1.81–3.56) < 0.001 1.47 (1.13–1.91) 0.004 1.96 (1.37–2.81) < 0.001 1.23 (1.09–1.39) 0.001 1.38 (1.19–1.60) < 0.001 1.87 (1.43–2.46) < 0.001 1.85 (1.32–2.60) < 0.001 1.68 (1.26–2.25) < 0.001 1.98 (1.38–2.82) < 0.001 2.98 (2.34–3.80) < 0.001 2.53 (1.87–3.44) < 0.001 3.28 (2.59–4.16) < 0.001 3.51 (2.61–4.71) < 0.001 P Sex Male vs female Age (per year of age) Smoking Yes vs no Marital status Married vs other Residential area Rural vs urban Distance to hospital Different province vs the same province Education level High vs low education level Occupational status Unemployed vs employed Average annual income ≥ $42,500 vs < $42,500 USD Insurance status No insurance vs any insurance Comorbidity Yes vs no Time to diagnosis > vs ≤1 month ISS stage III vs I/II LDH level ≥ 240 vs < 240 U/L Cytogenetics High risk vs standard risk Receipt of transplant No vs yes Regular treatment no vs yes PFS progression-free survival, OS overall survival, HR hazard ratio, CI confidence interval, ISS international staging system, LDH lactate dehydrogenase educational level of these patients have an interactive effect on survival, we conducted a hierarchical analysis of age The results of multivariate Cox regression analyses showed that education level was an independent factor affecting the prognosis of MM patients after adjustments were made for potential confounders Our results showed that patients with high education levels were more likely to have a longer PFS and OS Patients with high education levels were younger, and the time from onset of symptoms to diagnosis was shorter Those factors may result in patients in this subgroup having lower tumor loads (e.g., LDH levels and ISS stages) and fewer complications In addition, patients with high education levels were more likely to choose effective treatments, such as transplantation, than patients with low education levels, and these patients more often received regular treatment Therefore, the above factors may partly explain why education levels affect patient survival In addition, our results showed that patients with high education levels have financial and work support, and Xu et al BMC Cancer (2020) 20:737 Page of 10 Table Multivariate analysis of baseline parameters associated with PFS Variables PFS HR (95% CI) P Education level: high vs low 0.50 (0.34–0.72) < 0.001 LDH: ≥240 vs < 240 U/L 2.08 (1.48–2.94) < 0.001 Cytogenetics: high risk vs standard risk 1.77 (1.28–2.45) 0.001 Receipt of transplant: no vs yes 2.70 (1.95–3.74) < 0.001 Education level: high vs low 0.47 (0.29–0.74) 0.002 LDH: ≥240 vs < 240 U/L 2.45 (1.52–3.95) 0.001 Cytogenetics: high risk vs standard risk 1.85 (1.18–2.90) 0.007 Receipt of transplant: no vs yes 2.00 (1.20–3.35) 0.008 Regular treatment: no vs yes 2.08 (1.53–3.73) 0.015 Education level: high vs low 0.53 (0.29–0.98) 0.043 LDH: ≥240 vs < 240 U/L 1.81 (1.10–3.00) 0.020 Cytogenetics: high risk vs standard risk 1.68 (1.03–2.72) 0.037 Receipt of transplant: no vs yes 2.38 (1.36–4.17) 0.002 All patients Patients < 60 years Patients ≥ 60 years PFS progression-free survival, HR hazard ratio, CI confidence interval, LDH lactate dehydrogenase Table Multivariate analysis of baseline parameters associated with OS Variables OS HR (95% CI) P Age (per year of age) 1.03 (1.00–1.05) 0.028 Education level: high vs low 0.32 (0.19–0.56) < 0.001 LDH: ≥240 vs < 240 U/L 1.86 (1.18–2.94) 0.008 Cytogenetics: high risk vs standard risk 2.01 (1.32–3.06) 0.001 Comorbidity: yes vs no 2.01 (1.25–3.23) 0.004 Regular treatment: no vs yes 1.73 (1.08–2.77) 0.024 Education level: high vs low 0.30 (0.14–0.62) 0.001 Cytogenetics: high risk vs standard risk 2.37 (1.30–4.32) 0.005 Regular treatment: no vs yes 2.17 (1.08–4.38) 0.030 Education level: high vs low 0.26 (0.11–0.62) 0.002 LDH: ≥240 vs < 240 U/L 2.27 (1.24–4.18) 0.008 Cytogenetics: high risk vs standard risk 1.84 (1.01–3.33) 0.045 Comorbidity: yes vs no 3.16 (1.32–7.55) 0.010 All patients Patients < 60 years Patients ≥ 60 years OS overall survival, HR hazard ratio, CI confidence interval, LDH lactate dehydrogenase they tend to have more stable employment and income These factors may allow them to make treatment choices without cost restrictions and pay more attention to the efficacy of drugs so as to choose a more positive and effective treatment Similarly, Alter, D.A et al reported that compared to patients with lower SES, more affluent or better educated patients were more likely to undergo active and effective treatment [26] Additionally, insurance is also a very important economic factor, and we found that patients with high education levels are more likely to have insurance coverage Several studies have reported that insurance status was associated with OS, and patients who were uninsured had poorer survival than those who were insured [7, 27, 28] However, for patients with malignant tumors, the mechanism of the impact of education level on their survival is extremely complex Linder, G et al found that high education levels were associated with a greater probability of being offered curative treatment and improved survival in esophageal and gastroesophageal junctional cancer in Sweden; the reason may be communication difficulties and a lack of understanding of treatment, which were more commonly reported in groups with low education levels [29] This finding reflects that a high level of education can help patients gain a full understanding of their diseases and make it easier to acquire health-related knowledge Additionally, our study showed that patient education levels were related to treatment compliance, and there was also one report showed that patients with a high education level have better treatment compliance [30] Besides, some studies have shown that low education levels might undermine the patient’s initiative to seek healthcare services, leading to a delay in the diagnosis of a primary disease or a lifethreatening complication [31, 32] These factors also need to be fully taken into account Moreover, patient treatment can be managed according to their SES At present, new drugs (such as bortezomib and lenalidomide) and ASCT can significantly improve survival in patients with MM, but these methods result in a great increase in the cost of treatment [33] Therefore, drug-induced sequential ASCT is preferred for patients with high SES who are suitable for transplantation, and new drugs are preferred for patients with high SES who are not suitable for transplantation, while patients with low SES can choose less expensive options, such as regimens containing thalidomide combined with cyclophosphamide and dexamethasone Palliative treatment is more suitable for patients with severe complications who cannot tolerate chemotherapy than for patients with low SES Our research has some limitations owing to its retrospective nature In addition, some of the values were missing, but the proportion of missing values for most Xu et al BMC Cancer (2020) 20:737 variables was less than 10% In addition, we did not get the specific treatment details of these patients and there were many confounding variables in this study In the future, we can further analyze the relationship between the specific treatment regimens, treatment response, comorbidities and educational levels and survival prognosis Conclusions With continuous advancements in the treatment of multiple myeloma, the prognosis of patients has greatly improved However, not all patients benefit equally By analyzing the relationship between sociodemographic factors and the survival of patients with multiple myeloma in China, we found that education level is an independent factor affecting survival outcomes In particular, MM patients with high education levels have a better economic foundation, can seek medical treatment in a more timely manner, can choose the best treatment regimens and can be treated more regularly Therefore, the results of this study indicate that we can use the education level of newly diagnosed patients to evaluate the prognosis of these patients and to create more reasonable treatment plans Abbreviations MM: Multiple myeloma; ISS: International staging system; LDH: Lactate dehydrogenase; PFS: Progression-free survival; OS: Overall survival; SES: Socioeconomic status; HR: Hazard ratio; 95% CI: 95%confidence interval; FISH: Fluorescence in situ hybridization Acknowledgments This work was supported by Sun Yat-sen University medical clinical trial “5010 Plan” 2017005 Authors’ contributions JL came up with the study concept and design and was involved in editing and review LX collected the data, prepared and edited the manuscript and performed statistical analysis XW, XP, XW, QW, BW, JC, YZ, LC, and WL assisted with data acquisition All authors read and approved of the final manuscript Funding Not Applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available as presently we have not been granted permission by the institutional review board to so However, data can be made available from the corresponding author on reasonable request Ethics approval and consent to participate This study was reviewed and approved by the first affiliated hospital of Sun Yat-sen university (IRB:[2019]341) Due to retrospective design of the study, the requirement for informed consent was waived Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Page of 10 Author details Department of Hematology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China 2Department of Hematology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China 3Department of Hematology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China Department of Hematology, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China 5Department of Hematology, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, China 6Department of Hematology, Shunde Hospital of Southern Medical University, Shunde, Guangdong, China 7Department of Hematology, First People’s Hospital of Foshan, Foshan, Guangdong, China 8Department of Hematology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China Department of Internal Medicine, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China Received: 14 March 2020 Accepted: 13 July 2020 References Kumar SK, Rajkumar V, Kyle RA, et al Multiple myeloma Nat Rev Dis Primers 2017;3:17046 Rollig C, Knop S, Bornhauser M Multiple myeloma Lancet 2015;385(9983): 2197–208 Tan D, Chng WJ, Chou T, et al Management of multiple myeloma in Asia: resource-stratified guidelines Lancet Oncol 2013;14(12):e571–81 Siegel RL, Miller KD, Jemal A Cancer statistics, 2019 CA Cancer J Clin 2019; 69(1):7–34 Ailawadhi S, Bhatia K, Aulakh S, et al Equal treatment and outcomes for everyone with multiple myeloma: are we there yet? 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baseline parameters associated with OS Variables OS HR (95% CI) P Age (per year of age) 1.03 (1.00–1.05) 0.028 Education level: